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An analytical model for multi-tier internet services and its applications
- In Proc. of the ACM SIGMETRICS’2005
, 2005
"... Since many Internet applications employ a multi-tier architecture, in this paper, we focus on the problem of analytically modeling the behavior of such applications. We present a model based on a network of queues, where the queues represent different tiers of the application. Our model is sufficien ..."
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Cited by 185 (11 self)
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Since many Internet applications employ a multi-tier architecture, in this paper, we focus on the problem of analytically modeling the behavior of such applications. We present a model based on a network of queues, where the queues represent different tiers of the application. Our model is sufficiently general to capture (i) the behavior of tiers with significantly different performance characteristics and (ii) application idiosyncrasies such as session-based workloads, tier replication, load imbalances across replicas, and caching at intermediate tiers. We validate our model using real multi-tier applications running on a Linux server cluster. Our experiments indicate that our model faithfully captures the performance of these applications for a number of workloads and configurations. For a variety of scenarios, including those with caching at one of the application tiers, the average response times predicted by our model were within the 95 % confidence intervals of the observed average response times. Our experiments also demonstrate the utility of the model for dynamic capacity provisioning, performance prediction, bottleneck identification, and session policing. In one scenario, where the request arrival rate increased from less than 1500 to nearly 4200 requests/min, a dynamic provisioning technique employing our model was able to maintain response time targets by increasing the capacity of two of the application tiers by factors of 2 and 3.5, respectively.
Dynamic provisioning of multi-tier internet applications
- in Autonomic Computing, 2005. ICAC 2005. Proceedings. Second International Conference on, 2005
"... Dynamic capacity provisioning is a useful technique for handling the multi-time-scale variations seen in Internet workloads. In this paper, we propose a novel dynamic pro-visioning technique for multi-tier Internet applications that employs (i) a flexible queuing model to determine how much resource ..."
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Cited by 140 (9 self)
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Dynamic capacity provisioning is a useful technique for handling the multi-time-scale variations seen in Internet workloads. In this paper, we propose a novel dynamic pro-visioning technique for multi-tier Internet applications that employs (i) a flexible queuing model to determine how much resources to allocate to each tier of the application, and (ii) a combination of predictive and reactive methods that deter-mine when to provision these resources, both at large and small time scales. Our experiments on a forty-machine Linux-based hosting platform demonstrate the responsiveness of our technique in handling dynamic workloads. In one scenario where a flash crowd caused the workload of a three-tier ap-plication to double, our technique was able to double the ap-plication capacity within five minutes, thus maintaining re-sponse time targets.
Agile dynamic provisioning of multi-tier internet applications
- ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS (TAAS
, 2008
"... Dynamic capacity provisioning is a useful technique for handling the multi-time-scale variations seen in Internet workloads. In this paper, we propose a novel dynamic provisioning technique for multi-tier Internet applications that employs (i) a flexible queuing model to determine how much resourc ..."
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Cited by 88 (7 self)
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Dynamic capacity provisioning is a useful technique for handling the multi-time-scale variations seen in Internet workloads. In this paper, we propose a novel dynamic provisioning technique for multi-tier Internet applications that employs (i) a flexible queuing model to determine how much resources to allocate to each tier of the application, and (ii) a combination of predictive and reactive methods that determine when to provision these resources, both at large and small time scales. We propose a novel data center architecture based on virtual machine monitors to reduce provisioning overheads. Our experiments on a forty-machine Xen/Linux-based hosting platform demonstrate the responsiveness of our technique in handling dynamic workloads. In one scenario where a flash crowd caused the workload of a three-tier application to double, our technique was able to double the application capacity within five minutes, thus maintaining response time targets. Our technique also reduced the overhead of switching servers across applications from several minutes to less than a second, while meeting the performance targets of residual sessions.
A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
- In ICAC
, 2007
"... Abstract — The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective models for dynamic resource provisioning and for delivering quality of service to these applications ..."
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Cited by 83 (11 self)
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Abstract — The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making model adaptivity to the observed workload changes a critical requirement for model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then we use this approximation in an analytic model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for modeling diverse workloads with a changing transaction mix over time. Using the TPC-W benchmark and its three different transaction mixes we investigate factors that impact the efficiency and accuracy of the proposed performance prediction models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions. I.
Workload analysis and demand prediction of enterprise data center applications
, 2007
"... Abstract — Advances in virtualization technology are enabling the creation of resource pools of servers that permit multiple application workloads to share each server in the pool. Understanding the nature of enterprise workloads is crucial to properly designing and provisioning current and future s ..."
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Cited by 65 (7 self)
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Abstract — Advances in virtualization technology are enabling the creation of resource pools of servers that permit multiple application workloads to share each server in the pool. Understanding the nature of enterprise workloads is crucial to properly designing and provisioning current and future services in such pools. This paper considers issues of workload analysis, performance modeling, and capacity planning. Our goal is to automate the efficient use of resource pools when hosting large numbers of enterprise services. We use a trace based approach for capacity management that relies on i) the characterization of workload demand patterns, ii) the generation of synthetic workloads that predict future demands based on the patterns, and iii) a workload placement recommendation service. The accuracy of capacity planning predictions depends on our ability to characterize workload demand patterns, to recognize trends for expected changes in future demands, and to reflect business forecasts for otherwise unexpected changes in future demands. A workload analysis demonstrates the burstiness and repetitive nature of enterprise workloads. Workloads are automatically classified according to their periodic behavior. The similarity among repeated occurrences of patterns is evaluated. Synthetic workloads are generated from the patterns in a manner that maintains the periodic nature, burstiness, and trending behavior of the workloads. A case study involving six months of data for 139 enterprise applications is used to apply and evaluate the enterprise workload analysis and related capacity planning methods. The results show that when consolidating to 8 processor systems, we predicted future per-server required capacity to within one processor 95 % of the time. The accuracy of predictions for required capacity suggests that such resource savings can be achieved with little risk. I.
A.: A cost-aware elasticity provisioning system for the cloud
- In: ICDCS
"... Abstract—In this paper we present Kingfisher, a cost-aware system that provides efficient support for elasticity in the cloud by (i) leveraging multiple mechanisms to reduce the time to transition to new configurations, and (ii) optimizing the selection of a virtual server configuration that minimiz ..."
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Cited by 39 (0 self)
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Abstract—In this paper we present Kingfisher, a cost-aware system that provides efficient support for elasticity in the cloud by (i) leveraging multiple mechanisms to reduce the time to transition to new configurations, and (ii) optimizing the selection of a virtual server configuration that minimizes the cost. We have implemented a prototype of Kingfisher and have evaluated its efficacy on a laboratory cloud platform. Our experiments with varying application workloads demonstrate that Kingfisher is able to (i) decrease the cost of virtual server resources by as much as 24 % compared to the current costunaware approach, (ii) reduce by an order of magnitude the time to transition to a new configuration through multiple elasticity mechanisms in the cloud, and (iii), illustrate the opportunity for design alternatives which trade-off the cost of server resources with the time required to scale the application. I.
Burstiness in Multi-Tier Applications: Symptoms, Causes, and New Models
, 2008
"... Workload flows in enterprise systems that use the multi-tier paradigm are often characterized as bursty, i.e., exhibit a form of temporal dependence. Burstiness often results in dramatic degradation of the perceived user performance, which is extremely difficult to capture with existing capacity p ..."
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Cited by 33 (14 self)
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Workload flows in enterprise systems that use the multi-tier paradigm are often characterized as bursty, i.e., exhibit a form of temporal dependence. Burstiness often results in dramatic degradation of the perceived user performance, which is extremely difficult to capture with existing capacity planning models. The main reason behind this deficiency of traditional capacity planning models is that the user perceived performance is the result of the complex interaction of a very complex workload with a very complex system. In this paper, we propose a simple and effective methodology for detecting burstiness symptoms in multi-tier systems rather than identifying the low-level exact cause of burstiness as traditional models would require. We provide an effective way to incorporate this information into a surprisingly simple and effective modeling methodology. This new modeling methodology is based on the index of dispersion of the service process at a server, which is inferred by observing the number of completions within the concatenated busy periods of that server. The index of dispersion together with other measurements that reflect the “estimated” mean and the 95th
Mdcsim: A multi-tier data center simulation, platform
- in Cluster Computing and Workshops, 2009. CLUSTER ’09. IEEE International Conference on
, 2009
"... Abstract—Performance and power issues are becoming in-creasingly important in the design of large cluster based multi-tier data centers for supporting a multitude of services. Design and analysis of such large/complex distributed system often suffers from the lack of availability of an adequate phys ..."
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Cited by 31 (2 self)
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Abstract—Performance and power issues are becoming in-creasingly important in the design of large cluster based multi-tier data centers for supporting a multitude of services. Design and analysis of such large/complex distributed system often suffers from the lack of availability of an adequate physical infrastructure and the cost constraints especially in the academic community. With this motivation, this paper presents a compre-hensive, flexible and scalable simulation platform for in-depth analysis of multi-tier data centers. Designed as a pluggable three-level architecture, our simulator captures all the important design specifics of the underlying communication paradigm, kernel level scheduling artifacts, and the application level interactions among the tiers of a three-tier data center. The flexibility of the simulator is attributed to its ability in experimenting with different design alternatives in the three layers, and in analyzing
Autonomic Mix-Aware Provisioning for Non-Stationary Data Center Workloads
"... Online Internet applications see dynamic workloads that fluctuate over multiple time scales. This paper argues that the non-stationarity in Internet application workloads, which causes the request mix to change over time, can have a significant impact on the overall processing demands imposed on dat ..."
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Cited by 29 (0 self)
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Online Internet applications see dynamic workloads that fluctuate over multiple time scales. This paper argues that the non-stationarity in Internet application workloads, which causes the request mix to change over time, can have a significant impact on the overall processing demands imposed on data center servers. We propose a novel mix-aware dynamic provisioning technique that handles both the non-stationarity in the workload as well as changes in request volumes when allocating server capacity in Internet data centers. Our technique employs the k-means clustering algorithm to automatically determine the workload mix and a queuing model to predict the server capacity for a given workload mix. We implement a prototype provisioning system that incorporates our technique and experimentally evaluate its efficacy on a laboratory Linux data center running the TPC-W web benchmark. Our results show that our k-means clustering technique accurately captures workload mix changes in Internet applications. We also demonstrate that mix-aware dynamic provisioning eliminates SLA violations due to under-provisioning with non-stationary web workloads, and that it offers a better resource usage by reducing over-provisioning when compared to a baseline provisioning approach that only reacts to workload volume changes. We also present a case study of our provisioning approach on Amazon’s EC2 cloud platform. 1.
R-Capriccio: A Capacity Planning and Anomaly Detection Tool for Enterprise Services with Live Workloads
, 2007
"... As the complexity of IT systems increases, performance management and capacity planning become the largest and most difficult expenses to control. New methodologies and modeling techniques that explain large-system behavior and help predict their future performance are now needed to effectively tac ..."
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Cited by 23 (9 self)
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As the complexity of IT systems increases, performance management and capacity planning become the largest and most difficult expenses to control. New methodologies and modeling techniques that explain large-system behavior and help predict their future performance are now needed to effectively tackle the emerging performance issues. With the multi-tier architecture paradigm becoming an industry standard for developing scalable client-server applications, it is important to design effective and accurate performance prediction models of multi-tier applications under an enterprise production environment and a real workload mix. To accurately answer performance questions for an existing production system with a real workload mix, we design and implement a new capacity planning and anomaly detection tool, called R-Capriccio, that is based on the following three components: i) a Workload Profiler that exploits locality in existing enterprise web workloads and extracts a small set of most popular, core client transactions responsible for the majority of client requests in the system; ii) a Regression-based Solver that is used for deriving the CPU demand of each core transaction on a given hardware; and iii) an Analytical Model that is based on a network of queues that models a multi-tier system. To validate R-Capriccio, we conduct a detailed case study using the access logs from two heterogeneous production servers that represent customized client accesses to a popular and actively used HP Open View Service Desk application.
